Radiant dimensionality reduction mapping, clustering

factor analysis applicability

finding how many factors to explain enough from eigenvalues cumulative

factor analysis

principal component analysis (factor analysis without rotation)

factor analysis with varimax rotation

mapping from distance between 2 items

Multidimensional Scaling: MDS

mapping from item’s attributes vector

hierarchical clustering

K-means clustering

quantitative explained by multi ordninal variables

conjoint analysis

collaborative filtering recommendation model

prepare dataset for collaborative filtering
dataset has each user's rating for item
train data has complete rating for all items in each user
test data does not have rating for some item in each user

collaborative filtering caluculate a user's unknown ratings from the user's known rating for other items

tb0=attitude
names(tb0)=c('a0','a1','a2','a3','a4','a5','a6')
tb0$a4[26:30]=NA
tb0$a5[26:30]=NA
tb0$a6[26:30]=NA
tb0$train=c(rep(1,25),rep(0,5))
tb0=cbind(user=paste0('u',1:30),tb0)
tb=as_tibble(tb0)
tb=pivot_longer(tb, cols = c(-user,-train),
                names_to = 'item', values_to = 'rating')